Study

Question 1

Also known as the discovery phase

Answer 1

Business understanding

Question 2

Analyst defines the major questions of interest that need to be answered

Answer 2

Business understanding

Question 3

The phase of collecting data

Answer 3

Data acquisition

Question 4

Alternative names include data cleansing, data wrangling, data munging, and feature engineering

Answer 4

Data cleaning

Question 5

When ignored the results from analysis may be irrelevant
No one common tool, may use SQL, Python, R, or Excel
Data quality is measured in terms of uniqueness and relevance

Answer 5

Data cleaning

Question 6

Analyst begins to understand the basic nature of data and the relationships within
Often relies on visualization tools and numerical summaries such at central tendency and variability
Central tendency is a single value that attempts to describe a set of data by identifying the central position
Variability describes how far apart data points lie from each other and from the center of a distribution

Answer 6

Data exploration

Question 7

Creating models that enable predictions of outcomes of interest
Tools such as Python and R play an important role in automating the training and use of models

Answer 7

Predictive modeling

Question 8

Sometimes machine learning is used as a synonym

Answer 8

Data mining

Question 9

Ability of computers to look for patterns in large amounts of data
Tools such as Python and R play an important role

Answer 9

Data mining

Question 10

An analyst tells the story of the data and uses graphs or interactive dashboards to inform others of the findings from the analyses

Answer 10

Reporting and visualization

Question 11

The goal is to provide actionable insights for various stakeholders

Answer 11

Reporting and visualization

Question 12

Scope Project
Identify stakeholders and research questions/KPIs
Identify timeline, budget, and participants

Answer 12

Business Understanding

Question 13

Gather/collect data from a variety of sources
Provide structure to data accessible via relational databases (SQL)
Build data pipeline (ETL)
Use of API to download data from an external source

Answer 13

Data acquisition

Question 14

Estimate/project future values or likelihood of an event.
Extend correlations found in EDA to mathematical models
Predict/determine output values based on input values
Cross-validation of predictive models to ensure accuracy.

Answer 14

Predictive Modeling

Question 15

Creating training and testing datasets to build models from
Identify/detect patterns
Determine if groups (clusters) exist in data
Classify data into groups
Create models that “learn” and improve (e.g., machine/deep learning, AI, etc.)

Answer 15

Data mining

Question 16

Tell a story with data
Provide a summary of analytic analysis
Provide insights to stakeholders
Create insightful graphs that showcase trends and forecasts

Answer 16

Reporting and visualization

Question 17

What happened?

Answer 17

Descriptive Analytics

Question 18

Why did it happen?

Answer 18

Diagnostic Analytics

Question 19

What will happen?

Answer 19

Predictive Analytics

Question 20

How can we make it happen?

Answer 20

Predictive Analytics

Question 21

Is a relationship between two variables: when one variable changes, you know the degree in which the other variable changes

Answer 21

Correlation

Question 22

Is when there is a real-world explanation for why this is logically happening; it implies a cause and effect

Answer 22

Causation

Question 23

Which phase of the data analytics life cycle is also known as the discovery phase?

Answer 23

Business understanding

Question 24

Which phase of data analytics life cycle allows an analyst to use graphs or interactive dashboards to tell the story of the data?

Answer 24

Reporting and visualization

Question 25

Which phase of data analytics life cycle does the analyst begin to understand the nature of the data?

Answer 25

Data exploration

Question 26

Which phase of the data analytics life cycle provides structure to data accessible via relational databases?

Answer 26

Data acquisition

Question 27

Which term is defined as a relationship between two variables?

Answer 27

Correlation

Question 28

A way to graph numerical data in groups or bins that allow bars to represent frequencies

Answer 28

Histogram

Question 29

Provides a concise summary of the quartiles of numerical data (i.e., cut points that divide the data into 25% percentile segments)

Answer 29

Boxplot

Question 30

Colorful graph that can visually show frequency or interaction using a range of colors

Answer 30

Heatmap

Question 31

Two-dimensional graph
Great to visualize correlation or relationships

Answer 31

Scatterplot

Question 32

Predict an outcome based on a set of predictor variables

Answer 32

Regression

Question 33

Technique in which the analyst wants to assign an item to a specific category based on various conditions

Answer 33

Classification

Question 34

Groupings are unknown and the analyst wishes to determine if the objects belong to any groups

Answer 34

Clustering

Question 35

Looks for trends in data over time
Focused on breaking apart different reasons for the variation (decomposition)

Answer 35

Time series

Question 36

Technique attempts to group variables into meaning groups

Answer 36

Principal Component Analysis

Question 37

Tool - Data Science (Deep Learning/AI), Web Development, Embedded System

Answer 37

Python

Question 38

Tool - Data analysis and statistical modeling

Answer 38

R

Question 39

Tool - Can easily perform matrix computation as well as optimization

Answer 39

Python

Question 40

Tool - Consists of many to use packages

Answer 40

R

Question 41

Key Characteristic: Often numbers or labels, stored in a structured framework of columns and rows relating to pre-set parameters
Typical File Types: Databases

Answer 41

Structured Data File Type

Question 42

Key Characteristic: Loosely organized into categories using meta tags
Typical File Types: JSON, XML, Email, Web pages

Answer 42

Semi-structured Data File Type

Question 43

Key Characteristic: Text-heavy information that’s not organized in a clearly defined framework or model
Typical File Types: Audio, Video, Image data, Natural Language, Documents

Answer 43

Unstructured Data File Type